{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T14:58:17Z","timestamp":1761663497143,"version":"build-2065373602"},"reference-count":33,"publisher":"Institution of Engineering and Technology (IET)","issue":"12","license":[{"start":{"date-parts":[[2016,12,1]],"date-time":"2016-12-01T00:00:00Z","timestamp":1480550400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2016,12]]},"abstract":"<jats:p>Figure\u2010ground segmentation is used to extract the foreground from the background, where the foreground is usually defined as the region containing the most meaningful object of the image. In fact, the algorithms that take advantage of human\u2013computer interaction often attain better performance and they are based on the \u2018one\u2010to\u2010one\u2019 model. In this study, the authors present a novel algorithm for figure\u2010ground segmentation based on the GrabCut algorithm, which is a common segmentation algorithm that is user interactive. However, instead of a real user, they attempt to use a pre\u2010trained deep convolutional neural network to interact with GrabCut for completing its job successfully. Weizmann's segmentation evaluation database is used as the test dataset and the results show that their algorithm works well for figure\u2010ground segmentation. While the previous automatic segmentation algorithms are required to rank their segments empirically in order to find the position of the foreground after the segmentation, their algorithm is fully automatic.<\/jats:p>","DOI":"10.1049\/iet-ipr.2016.0009","type":"journal-article","created":{"date-parts":[[2016,7,7]],"date-time":"2016-07-07T12:42:18Z","timestamp":1467895338000},"page":"937-942","source":"Crossref","is-referenced-by-count":14,"title":["Fully automatic figure\u2010ground segmentation algorithm based on deep convolutional neural network and GrabCut"],"prefix":"10.1049","volume":"10","author":[{"given":"Ruigang","family":"Fu","sequence":"first","affiliation":[{"name":"ATR Key Laboratory National University of Defense Technology Changsha People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Li","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory National University of Defense Technology Changsha People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Gao","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory National University of Defense Technology Changsha People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory National University of Defense Technology Changsha People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2016,12]]},"reference":[{"key":"e_1_2_6_2_1","first-page":"30","volume-title":"What is a good image segment? 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